Question Answering (Q\&A) from structured data is a technique that may
revolutionize enterprise search.
A very promising use-case for such technology is Business Intelligence (BI).
%Data warehouses became an important facility for monitoring and decision
%making. 
In order to make BI more accessible to end-users, some
efforts have been made in the field of search for existing reports. However,
the problem of converting an end-user's natural language input to a valid
structured query in an ad-hoc fashion hasn't been sufficiently solved yet. 
%This
%is in particular important as todays' self-service BI tools for reporting are
%still too difficult to operate for non-technology affine business users.
%
%
In this paper we present a framework for Q\&A systems that operate on 
structured data. The main innovation is that the framework allows 
defining a mapping between recognized semantics of a user's questions 
to a structured query model that can be executed on 
arbitrary data sources. It bases on popular standards like RDF and SparQL 
and is therefore very easy to adapt to other domains or use-cases.
We will describe the application of this framework at hand of a BI question
answering use-case, which also includes the personalization of generated
queries, demonstrating the real-world applicability of our approach. In
our experiments, we demonstrate that with our approach one can easily 
achieve a similar answering quality as one of the most popular 
Q\&A systems on the Web.